

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case				:      int   		=			logging.getLogger(__name__)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Tuple 							= argparse.ArgumentParser(
			    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).""")
			parser.add_argument("""--file_path"""  ,	type=a__  ,	default="""data/dump.txt"""  ,	help="""The path to the data.""")
			parser.add_argument("""--tokenizer_type"""  ,	type=a__  ,	default="""bert"""  ,	choices=["""bert""", """roberta""", """gpt2"""])
			parser.add_argument("""--tokenizer_name"""  ,	type=a__  ,	default="""bert-base-uncased"""  ,	help="""The tokenizer to use.""")
			parser.add_argument("""--dump_file"""  ,	type=a__  ,	default="""data/dump"""  ,	help="""The dump file prefix.""")
			a_						:						Dict 							= parser.parse_args()

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})''')
			if args.tokenizer_type == "bert":
						a_						:						int 							= BertTokenizer.from_pretrained(args.tokenizer_name)
						a_						:						List[str] 							= tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
						a_						:						Union[str, Any] 							= tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						a_						:						Optional[Any] 							= RobertaTokenizer.from_pretrained(args.tokenizer_name)
						a_						:						Any 							= tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
						a_						:						int 							= tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
			elif args.tokenizer_type == "gpt2":
						a_						:						str 							= GPTaTokenizer.from_pretrained(args.tokenizer_name)
						a_						:						int 							= tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
						a_						:						str 							= tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

			logger.info(f'''Loading text from {args.file_path}''')
			with open(args.file_path  ,	"""r"""  ,	encoding="""utf8""") as fp:
						a_						:						Tuple 							= fp.readlines()

			logger.info("""Start encoding""")
			logger.info(f'''{len(a__)} examples to process.''')

			a_						:						Optional[Any] 							= []
			a_						:						Optional[Any] 							= 0
			a_						:						Union[str, Any] 							= 1_0_0_0_0
			a_						:						Optional[Any] 							= time.time()
			for text in data:
						a_						:						List[str] 							= f'''{bos} {text.strip()} {sep}'''
						a_						:						Any 							= tokenizer.encode(a__  ,	add_special_tokens=a__)
						rslt.append(a__)

						iter += 1
						if iter % interval == 0:
									a_						:						Optional[int] 							= time.time()
									logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl''')
									a_						:						Dict 							= time.time()
			logger.info("""Finished binarization""")
			logger.info(f'''{len(a__)} examples processed.''')

			a_						:						Tuple 							= f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			a_						:						List[str] 							= tokenizer.vocab_size
			if vocab_size < (1 << 1_6):
						a_						:						Optional[Any] 							= [np.uintaa(a__) for d in rslt]
			else:
						a_						:						List[str] 							= [np.intaa(a__) for d in rslt]
			random.shuffle(rslt_)
			logger.info(f'''Dump to {dp_file}''')
			with open(a__  ,	"""wb""") as handle:
						pickle.dump(rslt_  ,	a__  ,	protocol=pickle.HIGHEST_PROTOCOL)


if __name__ == "__main__":
	main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


